{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 批次标准化Batch Normalization",
   "id": "c29bf21335fd997b"
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-08-26T13:20:25.414474Z",
     "start_time": "2025-08-26T13:20:25.411436Z"
    }
   },
   "source": [
    "import time\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from d2l import torch as d2l\n",
    "from utils_09 import load_data_fashion_mnist\n",
    "from utils_09 import Accumulator\n",
    "from utils_09 import accuracy"
   ],
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-26T13:20:25.430562Z",
     "start_time": "2025-08-26T13:20:25.426995Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class ReshapeLayer(torch.nn.Module):\n",
    "    '''定义通用图像预处理层，这里将传入的图像转为C1W28H28的tensor'''\n",
    "    def forward(self,x:torch.utils.data.DataLoader):\n",
    "        return x.reshape(-1,1,28,28)"
   ],
   "id": "5fe509e96ec94c53",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-26T13:20:25.439757Z",
     "start_time": "2025-08-26T13:20:25.437419Z"
    }
   },
   "cell_type": "code",
   "source": "device = torch.device('cuda')",
   "id": "c38d75fb5ca9f7c4",
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-26T13:20:25.446769Z",
     "start_time": "2025-08-26T13:20:25.444761Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "cdcc255111944b7c",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "使用$ \\frac{Input_h - Kernel_h + 2 * padding}{stride} + 1 $计算核处理后大小",
   "id": "780aae1802a0635a"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-26T13:20:25.469288Z",
     "start_time": "2025-08-26T13:20:25.452418Z"
    }
   },
   "cell_type": "code",
   "source": [
    "## 定义使用batch normalization layer改进后的模型结构\n",
    "net = nn.Sequential(\n",
    "    ReshapeLayer(),\n",
    "    nn.Conv2d(in_channels=1,out_channels=6,kernel_size=5,padding=2), ## 设置输入通道为1，输出通道为6，卷积核为5*5 填充为2\n",
    "    nn.BatchNorm2d(6),\n",
    "    nn.ReLU(),\n",
    "    nn.AvgPool2d(kernel_size=2,stride=2),## 设置池化核为2*2，步长为2\n",
    "    nn.Conv2d(in_channels=6,out_channels=16,kernel_size=5),\n",
    "    nn.BatchNorm2d(16),\n",
    "    nn.ReLU(),\n",
    "    nn.AvgPool2d(kernel_size=2,stride=2),\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(in_features=16*5*5,out_features = 120),\n",
    "    nn.BatchNorm1d(120),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(in_features = 120,out_features = 84),\n",
    "    nn.BatchNorm1d(84),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(84,10)\n",
    ")\n",
    "net.to(device)"
   ],
   "id": "3bccab017e9dffae",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): ReshapeLayer()\n",
       "  (1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
       "  (2): BatchNorm2d(6, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (3): ReLU()\n",
       "  (4): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "  (5): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (6): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (7): ReLU()\n",
       "  (8): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "  (9): Flatten(start_dim=1, end_dim=-1)\n",
       "  (10): Linear(in_features=400, out_features=120, bias=True)\n",
       "  (11): BatchNorm1d(120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (12): ReLU()\n",
       "  (13): Linear(in_features=120, out_features=84, bias=True)\n",
       "  (14): BatchNorm1d(84, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (15): ReLU()\n",
       "  (16): Linear(in_features=84, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-26T13:20:25.501609Z",
     "start_time": "2025-08-26T13:20:25.474297Z"
    }
   },
   "cell_type": "code",
   "source": [
    "batch_size = 256\n",
    "train_iter , test_iter = load_data_fashion_mnist(batch_size,cpu_workers=4)"
   ],
   "id": "ad95a0a5c3eb0ee4",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-26T13:20:25.512953Z",
     "start_time": "2025-08-26T13:20:25.509612Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def evaluate_accuracy_gpu(net:nn.Sequential,data_iter:torch.utils.data.DataLoader,device=None):\n",
    "    net.eval()\n",
    "    metric = Accumulator(2)\n",
    "    for X,y in data_iter:\n",
    "        X = X.to(device)\n",
    "        y = y.to(device)\n",
    "        metric.add(accuracy(net(X),y),y.numel())\n",
    "    return metric[0]/metric[1]"
   ],
   "id": "bd7edb1312c94302",
   "outputs": [],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-26T13:20:25.523956Z",
     "start_time": "2025-08-26T13:20:25.518957Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def init_weights(m):\n",
    "    if isinstance(m,nn.Linear) or isinstance(m,nn.Conv2d):\n",
    "        nn.init.xavier_normal_(m.weight)\n",
    "net.apply(init_weights)"
   ],
   "id": "5311108f3f4b661a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): ReshapeLayer()\n",
       "  (1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
       "  (2): BatchNorm2d(6, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (3): ReLU()\n",
       "  (4): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "  (5): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (6): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (7): ReLU()\n",
       "  (8): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "  (9): Flatten(start_dim=1, end_dim=-1)\n",
       "  (10): Linear(in_features=400, out_features=120, bias=True)\n",
       "  (11): BatchNorm1d(120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (12): ReLU()\n",
       "  (13): Linear(in_features=120, out_features=84, bias=True)\n",
       "  (14): BatchNorm1d(84, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (15): ReLU()\n",
       "  (16): Linear(in_features=84, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-26T13:33:30.580809Z",
     "start_time": "2025-08-26T13:20:25.535961Z"
    }
   },
   "cell_type": "code",
   "source": [
    "lr = 0.1\n",
    "optimizer = torch.optim.SGD(net.parameters(),lr = lr)\n",
    "loss = nn.CrossEntropyLoss()\n",
    "start_time = time.time()\n",
    "num_epochs = 100\n",
    "train_loss_record = list()\n",
    "train_acc_record = list()\n",
    "test_acc_record = list()\n",
    "for epoch in range(num_epochs):\n",
    "    metric = Accumulator(3)\n",
    "    net.train()\n",
    "    for X,y in train_iter:\n",
    "        optimizer.zero_grad()\n",
    "        X = X.to(device)\n",
    "        y = y.to(device)\n",
    "        y_predict = net(X)\n",
    "        l = loss(y_predict,y)\n",
    "        l.backward()\n",
    "        optimizer.step()\n",
    "        metric.add(l * X.shape[0],accuracy(y_predict,y),X.shape[0])\n",
    "    train_metric = (metric[0]/metric[2],metric[1]/metric[2])\n",
    "    test_acc = evaluate_accuracy_gpu(net,test_iter,device)\n",
    "    print(f\"epoch {epoch}, train loss : {train_metric[0]}, train acc : {train_metric[1]},test_acc : {test_acc}\")\n",
    "    train_loss_record.append(train_metric[0])\n",
    "    train_acc_record.append(train_metric[1])\n",
    "    test_acc_record.append(test_acc)\n",
    "elapsed_time = time.time() - start_time\n",
    "print(f'total elapsed {elapsed_time} seconds')"
   ],
   "id": "b17cbdc8513a6489",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0, train loss : 0.5856071425120036, train acc : 0.7950833333333334,test_acc : 0.8081\n",
      "epoch 1, train loss : 0.3755742965062459, train acc : 0.8631666666666666,test_acc : 0.8348\n",
      "epoch 2, train loss : 0.3234359521865845, train acc : 0.88305,test_acc : 0.8127\n",
      "epoch 3, train loss : 0.2953348028182983, train acc : 0.8934166666666666,test_acc : 0.8299\n",
      "epoch 4, train loss : 0.27715713675816855, train acc : 0.8988833333333334,test_acc : 0.8607\n",
      "epoch 5, train loss : 0.26051214650472004, train acc : 0.90375,test_acc : 0.8855\n",
      "epoch 6, train loss : 0.24645714619954426, train acc : 0.90895,test_acc : 0.8923\n",
      "epoch 7, train loss : 0.23571573365529377, train acc : 0.9144666666666666,test_acc : 0.8811\n",
      "epoch 8, train loss : 0.22555261112848918, train acc : 0.91715,test_acc : 0.8774\n",
      "epoch 9, train loss : 0.21625575745900472, train acc : 0.9196333333333333,test_acc : 0.8974\n",
      "epoch 10, train loss : 0.20880458463033041, train acc : 0.9219666666666667,test_acc : 0.892\n",
      "epoch 11, train loss : 0.2004607014020284, train acc : 0.92545,test_acc : 0.8534\n",
      "epoch 12, train loss : 0.1935792827606201, train acc : 0.9286833333333333,test_acc : 0.8974\n",
      "epoch 13, train loss : 0.18729048465092976, train acc : 0.9309,test_acc : 0.8715\n",
      "epoch 14, train loss : 0.18332954676946003, train acc : 0.93225,test_acc : 0.8903\n",
      "epoch 15, train loss : 0.17607599194844564, train acc : 0.93575,test_acc : 0.8476\n",
      "epoch 16, train loss : 0.17095360606511434, train acc : 0.9372,test_acc : 0.8947\n",
      "epoch 17, train loss : 0.16384214007059733, train acc : 0.9405333333333333,test_acc : 0.8802\n",
      "epoch 18, train loss : 0.1594768243630727, train acc : 0.94175,test_acc : 0.9071\n",
      "epoch 19, train loss : 0.15340530420939127, train acc : 0.9435,test_acc : 0.874\n",
      "epoch 20, train loss : 0.1511009523232778, train acc : 0.9445,test_acc : 0.9005\n",
      "epoch 21, train loss : 0.14634050019582112, train acc : 0.9470166666666666,test_acc : 0.9038\n",
      "epoch 22, train loss : 0.14183621349334716, train acc : 0.9485,test_acc : 0.8984\n",
      "epoch 23, train loss : 0.13744105874697368, train acc : 0.94975,test_acc : 0.878\n",
      "epoch 24, train loss : 0.1328342616399129, train acc : 0.95175,test_acc : 0.8385\n",
      "epoch 25, train loss : 0.12819382626215617, train acc : 0.9529833333333333,test_acc : 0.8599\n",
      "epoch 26, train loss : 0.12475476589202882, train acc : 0.9538833333333333,test_acc : 0.8189\n",
      "epoch 27, train loss : 0.12274106259346008, train acc : 0.9541333333333334,test_acc : 0.8296\n",
      "epoch 28, train loss : 0.1168014197031657, train acc : 0.9582666666666667,test_acc : 0.8997\n",
      "epoch 29, train loss : 0.11394064362843831, train acc : 0.9581333333333333,test_acc : 0.8876\n",
      "epoch 30, train loss : 0.10816337842941284, train acc : 0.9607333333333333,test_acc : 0.9085\n",
      "epoch 31, train loss : 0.10575056783358255, train acc : 0.9618166666666667,test_acc : 0.8793\n",
      "epoch 32, train loss : 0.10214978895187378, train acc : 0.9624666666666667,test_acc : 0.8797\n",
      "epoch 33, train loss : 0.09974397419293722, train acc : 0.9643333333333334,test_acc : 0.9053\n",
      "epoch 34, train loss : 0.09712781330744426, train acc : 0.9650666666666666,test_acc : 0.8774\n",
      "epoch 35, train loss : 0.09301185836791992, train acc : 0.9661833333333333,test_acc : 0.8939\n",
      "epoch 36, train loss : 0.08811694725354513, train acc : 0.9693166666666667,test_acc : 0.8171\n",
      "epoch 37, train loss : 0.08879200642903645, train acc : 0.9679166666666666,test_acc : 0.9053\n",
      "epoch 38, train loss : 0.08435090646743774, train acc : 0.9700833333333333,test_acc : 0.9028\n",
      "epoch 39, train loss : 0.08220354924201966, train acc : 0.9713833333333334,test_acc : 0.8946\n",
      "epoch 40, train loss : 0.07959864797592163, train acc : 0.9712666666666666,test_acc : 0.8864\n",
      "epoch 41, train loss : 0.07749774338404337, train acc : 0.9727833333333333,test_acc : 0.8983\n",
      "epoch 42, train loss : 0.07386893946329752, train acc : 0.9745666666666667,test_acc : 0.8734\n",
      "epoch 43, train loss : 0.06989987740516662, train acc : 0.9753,test_acc : 0.8996\n",
      "epoch 44, train loss : 0.07045438810984293, train acc : 0.9751333333333333,test_acc : 0.8971\n",
      "epoch 45, train loss : 0.06484243732293447, train acc : 0.9773,test_acc : 0.8972\n",
      "epoch 46, train loss : 0.06434735922018688, train acc : 0.9770333333333333,test_acc : 0.9048\n",
      "epoch 47, train loss : 0.06425485485394795, train acc : 0.9778166666666667,test_acc : 0.8956\n",
      "epoch 48, train loss : 0.060197192637125654, train acc : 0.9785833333333334,test_acc : 0.9035\n",
      "epoch 49, train loss : 0.059197130727767944, train acc : 0.9793166666666666,test_acc : 0.8982\n",
      "epoch 50, train loss : 0.054222077023983004, train acc : 0.9813333333333333,test_acc : 0.9045\n",
      "epoch 51, train loss : 0.05274254251718521, train acc : 0.9817166666666667,test_acc : 0.8983\n",
      "epoch 52, train loss : 0.04977701873779297, train acc : 0.9834666666666667,test_acc : 0.9096\n",
      "epoch 53, train loss : 0.04847118496100108, train acc : 0.9838833333333333,test_acc : 0.9055\n",
      "epoch 54, train loss : 0.04751920076608658, train acc : 0.98395,test_acc : 0.8304\n",
      "epoch 55, train loss : 0.04763661069869995, train acc : 0.9844,test_acc : 0.894\n",
      "epoch 56, train loss : 0.04502201935450236, train acc : 0.98515,test_acc : 0.8881\n",
      "epoch 57, train loss : 0.0435036164522171, train acc : 0.9862666666666666,test_acc : 0.8832\n",
      "epoch 58, train loss : 0.04056709825992584, train acc : 0.9875,test_acc : 0.9026\n",
      "epoch 59, train loss : 0.0406485000650088, train acc : 0.9866166666666667,test_acc : 0.8587\n",
      "epoch 60, train loss : 0.03919030669530233, train acc : 0.98715,test_acc : 0.9078\n",
      "epoch 61, train loss : 0.03782902202208837, train acc : 0.9874666666666667,test_acc : 0.9052\n",
      "epoch 62, train loss : 0.03395791259209315, train acc : 0.9897166666666667,test_acc : 0.9113\n",
      "epoch 63, train loss : 0.0346053605278333, train acc : 0.9889,test_acc : 0.8973\n",
      "epoch 64, train loss : 0.032440814836819966, train acc : 0.9899833333333333,test_acc : 0.8812\n",
      "epoch 65, train loss : 0.03238840590318044, train acc : 0.9897,test_acc : 0.9064\n",
      "epoch 66, train loss : 0.033960273555914564, train acc : 0.98935,test_acc : 0.8796\n",
      "epoch 67, train loss : 0.032408494464556376, train acc : 0.98995,test_acc : 0.8944\n",
      "epoch 68, train loss : 0.02745322410662969, train acc : 0.9918833333333333,test_acc : 0.886\n",
      "epoch 69, train loss : 0.027748844412962596, train acc : 0.9913833333333333,test_acc : 0.9116\n",
      "epoch 70, train loss : 0.025788007871309915, train acc : 0.9924833333333334,test_acc : 0.8835\n",
      "epoch 71, train loss : 0.023683335542678832, train acc : 0.9932333333333333,test_acc : 0.9044\n",
      "epoch 72, train loss : 0.02534025969306628, train acc : 0.9927166666666667,test_acc : 0.8989\n",
      "epoch 73, train loss : 0.022317471541961034, train acc : 0.9935166666666667,test_acc : 0.9053\n",
      "epoch 74, train loss : 0.024818031086524327, train acc : 0.9927333333333334,test_acc : 0.8933\n",
      "epoch 75, train loss : 0.022463250410556794, train acc : 0.99345,test_acc : 0.8853\n",
      "epoch 76, train loss : 0.02218884157538414, train acc : 0.9933333333333333,test_acc : 0.8802\n",
      "epoch 77, train loss : 0.021017869118849435, train acc : 0.99385,test_acc : 0.8865\n",
      "epoch 78, train loss : 0.021024706623951592, train acc : 0.9935333333333334,test_acc : 0.9003\n",
      "epoch 79, train loss : 0.01781490803261598, train acc : 0.9953833333333333,test_acc : 0.8996\n",
      "epoch 80, train loss : 0.01585470685561498, train acc : 0.9960166666666667,test_acc : 0.9061\n",
      "epoch 81, train loss : 0.015631915201743445, train acc : 0.9961666666666666,test_acc : 0.9093\n",
      "epoch 82, train loss : 0.0188465424567461, train acc : 0.9946,test_acc : 0.9029\n",
      "epoch 83, train loss : 0.019339168932040532, train acc : 0.99455,test_acc : 0.9038\n",
      "epoch 84, train loss : 0.015456690553824107, train acc : 0.9958666666666667,test_acc : 0.9042\n",
      "epoch 85, train loss : 0.01270627246995767, train acc : 0.99735,test_acc : 0.9031\n",
      "epoch 86, train loss : 0.013846526530385017, train acc : 0.9965833333333334,test_acc : 0.9106\n",
      "epoch 87, train loss : 0.012784688988327981, train acc : 0.9968,test_acc : 0.9094\n",
      "epoch 88, train loss : 0.011534358867009481, train acc : 0.99775,test_acc : 0.8804\n",
      "epoch 89, train loss : 0.012026098548372586, train acc : 0.9973833333333333,test_acc : 0.8932\n",
      "epoch 90, train loss : 0.014828456891576448, train acc : 0.9956666666666667,test_acc : 0.9056\n",
      "epoch 91, train loss : 0.010147063809633256, train acc : 0.9979833333333333,test_acc : 0.9018\n",
      "epoch 92, train loss : 0.009728919594486555, train acc : 0.9981666666666666,test_acc : 0.9006\n",
      "epoch 93, train loss : 0.010039946853121122, train acc : 0.9979166666666667,test_acc : 0.9058\n",
      "epoch 94, train loss : 0.011946507820487022, train acc : 0.99715,test_acc : 0.9075\n",
      "epoch 95, train loss : 0.009450627748171488, train acc : 0.9979833333333333,test_acc : 0.884\n",
      "epoch 96, train loss : 0.013316740693648656, train acc : 0.9964833333333334,test_acc : 0.8867\n",
      "epoch 97, train loss : 0.011449509820342063, train acc : 0.99715,test_acc : 0.9012\n",
      "epoch 98, train loss : 0.009336492769420147, train acc : 0.9981166666666667,test_acc : 0.9088\n",
      "epoch 99, train loss : 0.01247416521012783, train acc : 0.9967166666666667,test_acc : 0.9101\n",
      "total elapsed 785.0390141010284 seconds\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-26T13:33:30.810941Z",
     "start_time": "2025-08-26T13:33:30.658494Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from matplotlib import pyplot as plt\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(131)\n",
    "ax1.plot(range(num_epochs),train_acc_record)\n",
    "ax1.set_xlabel('epoch')\n",
    "ax1.set_ylabel('acc')\n",
    "ax2 = fig.add_subplot(132)\n",
    "ax2.plot(range(num_epochs),train_loss_record)\n",
    "ax2.set_xlabel('epoch')\n",
    "ax2.set_ylabel('loss')\n",
    "ax3 = fig.add_subplot(133)\n",
    "ax3.plot(range(num_epochs),test_acc_record)\n",
    "ax3.set_xlabel('epoch')\n",
    "ax3.set_ylabel('test acc')\n",
    "plt.show()"
   ],
   "id": "83e87e4584683aad",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-26T13:33:35.057410Z",
     "start_time": "2025-08-26T13:33:30.817839Z"
    }
   },
   "cell_type": "code",
   "source": [
    "for X, y in test_iter:\n",
    "    break\n",
    "def get_mnist_labels(labels:list[int]) -> list[str]:\n",
    "    \"\"\"返回mnist数据集标签文本\"\"\"\n",
    "    text_labels = ['t-shirt','trouser','pullover','dress','coat','sandal','shirt','sneaker','bag','ankle boot']\n",
    "    return [text_labels[int(i)] for i in labels]\n",
    "X = X[0:10].to(device)\n",
    "net.eval()\n",
    "with torch.no_grad():\n",
    "    y_hat = net(X[:10])\n",
    "pred_indexs = y_hat.argmax(axis=1).cpu().numpy()\n",
    "true_index = y.numpy()\n",
    "# 可视化\n",
    "pred_titles = get_mnist_labels(pred_indexs)\n",
    "true_titles = get_mnist_labels(true_index)\n",
    "images = X[:10].cpu().reshape(10, 28, 28)\n",
    "\n",
    "figsize = (10 * 20, 1 * 70)\n",
    "_, axes = plt.subplots(10, 1, figsize=figsize)\n",
    "axes = axes.flatten()\n",
    "for i, (ax, img) in enumerate(zip(axes,images)):\n",
    "    ax.imshow(img.numpy(),cmap='gray')\n",
    "    ax.set_title(f\"Pred: {pred_titles[i]}\\nTrue: {true_titles[i]}\")"
   ],
   "id": "9d4402af76343ae6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 20000x7000 with 10 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 18
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
